方法对比
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| 广义线性模型 (GLM)× | 广义可加模型 (GAM)× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 1972 | 1986 |
| 提出者≠ | John A. Nelder & Robert W. M. Wedderburn | Trevor Hastie & Robert Tibshirani |
| 类型≠ | Regression framework | Semi-parametric additive regression model |
| 开创性文献≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ |
| 别名 | GLM, generalized regression, exponential family regression, link-function model | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model |
| 相关≠ | 6 | 4 |
| 摘要≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. |
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